From d8999299494a0faf5047dcb1db60cf337edc2c4f Mon Sep 17 00:00:00 2001
From: viktorvaladi <viktor.valadi@hotmail.se>
Date: Thu, 18 Apr 2024 14:04:16 +0200
Subject: [PATCH 1/5] update flwr docs

---
 examples/flower-client/README.rst             |  27 ++++-
 examples/flower-client/client/entrypoint.py   |   2 +-
 .../client835-20240418-111128/entrypoint.py   | 107 ++++++++++++++++++
 .../client835-20240418-111128/fedn.yaml       |   8 ++
 .../client835-20240418-111128/flwr_client.py  |  41 +++++++
 .../client835-20240418-111128/flwr_task.py    |  95 ++++++++++++++++
 .../client835-20240418-111128/python_env.yaml |  11 ++
 examples/flower-client/package/entrypoint.py  | 107 ++++++++++++++++++
 examples/flower-client/package/fedn.yaml      |   8 ++
 examples/flower-client/package/flwr_client.py |  41 +++++++
 examples/flower-client/package/flwr_task.py   |  95 ++++++++++++++++
 .../flower-client/package/python_env.yaml     |  11 ++
 12 files changed, 548 insertions(+), 5 deletions(-)
 create mode 100755 examples/flower-client/client835-20240418-111128/entrypoint.py
 create mode 100644 examples/flower-client/client835-20240418-111128/fedn.yaml
 create mode 100644 examples/flower-client/client835-20240418-111128/flwr_client.py
 create mode 100644 examples/flower-client/client835-20240418-111128/flwr_task.py
 create mode 100644 examples/flower-client/client835-20240418-111128/python_env.yaml
 create mode 100755 examples/flower-client/package/entrypoint.py
 create mode 100644 examples/flower-client/package/fedn.yaml
 create mode 100644 examples/flower-client/package/flwr_client.py
 create mode 100644 examples/flower-client/package/flwr_task.py
 create mode 100644 examples/flower-client/package/python_env.yaml

diff --git a/examples/flower-client/README.rst b/examples/flower-client/README.rst
index 2093caf3f..364c80d3a 100644
--- a/examples/flower-client/README.rst
+++ b/examples/flower-client/README.rst
@@ -1,4 +1,4 @@
-Using Flower ClientApps in FEDn
+FEDn Project: Flower ClientApps in FEDn
 -------------------------------
 
 This example demonstrates how to run a Flower 'ClientApp' on FEDn.
@@ -62,9 +62,7 @@ On your local machine / client, start the FEDn client:
 
 .. code-block::
 
-   export FEDN_AUTH_SCHEME=Bearer
-   export FEDN_PACKAGE_EXTRACT_DIR=package
-   CLIENT_NUMBER=0 fedn run client -in client.yaml --secure=True --force-ssl
+   fedn run client -in client.yaml --secure=True --force-ssl
 
 
 Or, if you prefer to use Docker (this might take a long time):
@@ -110,3 +108,24 @@ Then start the client (using Docker)
    -e FEDN_AUTH_SCHEME=Bearer \
    -e FEDN_PACKAGE_EXTRACT_DIR=package \
    ghcr.io/scaleoutsystems/fedn/fedn:master run client -in client.yaml
+
+
+Scaling to multiple clients
+------------------------------------------------------------------
+
+To scale the experiment with additional clients on the same host, simply execute the run 
+command again from another terminal. If running from another host add another 'client.yaml', 
+install fedn and execute the run command. In both cases inject a client number as an environment 
+varible which is used for distributing data (see 'flwr_task.py').
+
+For Unix Operating Systems:
+
+.. code-block::
+
+   CLIENT_NUMBER=0 fedn run client -in client.yaml --secure=True --force-ssl
+
+Using Windows PowerShell:
+
+.. code-block::
+
+   & { $env:CLIENT_NUMBER="0"; fedn run client -in client.yaml --secure=$true --force-ssl }
diff --git a/examples/flower-client/client/entrypoint.py b/examples/flower-client/client/entrypoint.py
index 24f20773e..e80e7d4b5 100755
--- a/examples/flower-client/client/entrypoint.py
+++ b/examples/flower-client/client/entrypoint.py
@@ -30,7 +30,7 @@ def save_parameters(out_path, parameters_np):
     helper.save(parameters_np, out_path)
 
 
-def init_seed(out_path="seed.npz"):
+def init_seed(out_path="../seed.npz"):
     """Initialize seed model and save it to file.
 
     :param out_path: The path to save the seed model to.
diff --git a/examples/flower-client/client835-20240418-111128/entrypoint.py b/examples/flower-client/client835-20240418-111128/entrypoint.py
new file mode 100755
index 000000000..24f20773e
--- /dev/null
+++ b/examples/flower-client/client835-20240418-111128/entrypoint.py
@@ -0,0 +1,107 @@
+import os
+
+import fire
+from flwr_client import app
+
+from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
+from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+
+HELPER_MODULE = "numpyhelper"
+helper = get_helper(HELPER_MODULE)
+
+flwr_adapter = FlwrClientAppAdapter(app)
+
+
+def _get_node_id():
+    """Get client number from environment variable."""
+
+    number = os.environ.get("CLIENT_NUMBER", "0")
+    return int(number)
+
+
+def save_parameters(out_path, parameters_np):
+    """Save model paramters to file.
+
+    :param model: The model to serialize.
+    :type model: torch.nn.Module
+    :param out_path: The path to save to.
+    :type out_path: str
+    """
+    helper.save(parameters_np, out_path)
+
+
+def init_seed(out_path="seed.npz"):
+    """Initialize seed model and save it to file.
+
+    :param out_path: The path to save the seed model to.
+    :type out_path: str
+    """
+    # This calls get_parameters in the flower client which needs to be implemented.
+    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
+    save_parameters(out_path, parameters_np)
+
+
+def train(in_model_path, out_model_path):
+    """Complete a model update.
+
+    Load model paramters from in_model_path (managed by the FEDn client),
+    perform a model update through the flower client, and write updated paramters
+    to out_model_path (picked up by the FEDn client).
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_model_path: The path to save the output model to.
+    :type out_model_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    # Train on flower client
+    params, num_examples = flwr_adapter.train(
+        parameters=parameters_np, partition_id=_get_node_id(), config={}
+    )
+
+    # Metadata needed for aggregation server side
+    metadata = {
+        # num_examples are mandatory
+        "num_examples": num_examples,
+    }
+
+    # Save JSON metadata file (mandatory)
+    save_metadata(metadata, out_model_path)
+
+    # Save model update (mandatory)
+    save_parameters(out_model_path, params)
+
+
+def validate(in_model_path, out_json_path, data_path=None):
+    """Validate model on the clients test dataset.
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_json_path: The path to save the output JSON to.
+    :type out_json_path: str
+    :param data_path: The path to the data file.
+    :type data_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
+
+    # JSON schema
+    report = {
+        "test_loss": loss,
+        "test_accuracy": accuracy,
+    }
+    print(f"Loss: {loss}, accuracy: {accuracy}")
+    # Save JSON
+    save_metrics(report, out_json_path)
+
+
+if __name__ == "__main__":
+    fire.Fire(
+        {
+            "init_seed": init_seed,
+            "train": train,
+            "validate": validate,
+        }
+    )
diff --git a/examples/flower-client/client835-20240418-111128/fedn.yaml b/examples/flower-client/client835-20240418-111128/fedn.yaml
new file mode 100644
index 000000000..9fb33f18f
--- /dev/null
+++ b/examples/flower-client/client835-20240418-111128/fedn.yaml
@@ -0,0 +1,8 @@
+python_env: python_env.yaml
+entry_points:
+  build:
+    command: python entrypoint.py init_seed
+  train:
+    command: python entrypoint.py train
+  validate:
+    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/client835-20240418-111128/flwr_client.py b/examples/flower-client/client835-20240418-111128/flwr_client.py
new file mode 100644
index 000000000..297df3ca7
--- /dev/null
+++ b/examples/flower-client/client835-20240418-111128/flwr_client.py
@@ -0,0 +1,41 @@
+"""Flower client code using the ClientApp abstraction.
+Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
+"""
+
+from flwr.client import ClientApp, NumPyClient
+from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
+                       train)
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+    def __init__(self, cid) -> None:
+        super().__init__()
+        self.net = Net().to(DEVICE)
+        self.trainloader, self.testloader = load_data(
+            partition_id=int(cid), num_clients=10
+        )
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def fit(self, parameters, config):
+        set_weights(self.net, parameters)
+        train(self.net, self.trainloader, epochs=3)
+        return get_weights(self.net), len(self.trainloader.dataset), {}
+
+    def evaluate(self, parameters, config):
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.testloader)
+        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient(cid).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+)
diff --git a/examples/flower-client/client835-20240418-111128/flwr_task.py b/examples/flower-client/client835-20240418-111128/flwr_task.py
new file mode 100644
index 000000000..dea53d9cc
--- /dev/null
+++ b/examples/flower-client/client835-20240418-111128/flwr_task.py
@@ -0,0 +1,95 @@
+"""Flower client code for helper functions.
+Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
+"""
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from flwr_datasets import FederatedDataset
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, Normalize, ToTensor
+from tqdm import tqdm
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+
+    def __init__(self) -> None:
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def load_data(partition_id, num_clients):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose(
+        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
+
+
+def train(net, trainloader, epochs):
+    """Train the model on the training set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    for _ in range(epochs):
+        for batch in tqdm(trainloader, "Training"):
+            images = batch["img"]
+            labels = batch["label"]
+            optimizer.zero_grad()
+            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
+            optimizer.step()
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for batch in tqdm(testloader, "Testing"):
+            images = batch["img"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def get_weights(net):
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+def set_weights(net, parameters):
+    params_dict = zip(net.state_dict().keys(), parameters)
+    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/client835-20240418-111128/python_env.yaml b/examples/flower-client/client835-20240418-111128/python_env.yaml
new file mode 100644
index 000000000..b06804313
--- /dev/null
+++ b/examples/flower-client/client835-20240418-111128/python_env.yaml
@@ -0,0 +1,11 @@
+name: flower-client
+build_dependencies:
+  - pip
+  - setuptools
+  - wheel==0.37.1
+dependencies:
+  - torch==2.2.1
+  - torchvision==0.17.1
+  - fire==0.3.1
+  - fedn[flower]==0.9.0b2
+  - flwr-datasets[vision]==0.1.0
\ No newline at end of file
diff --git a/examples/flower-client/package/entrypoint.py b/examples/flower-client/package/entrypoint.py
new file mode 100755
index 000000000..24f20773e
--- /dev/null
+++ b/examples/flower-client/package/entrypoint.py
@@ -0,0 +1,107 @@
+import os
+
+import fire
+from flwr_client import app
+
+from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
+from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+
+HELPER_MODULE = "numpyhelper"
+helper = get_helper(HELPER_MODULE)
+
+flwr_adapter = FlwrClientAppAdapter(app)
+
+
+def _get_node_id():
+    """Get client number from environment variable."""
+
+    number = os.environ.get("CLIENT_NUMBER", "0")
+    return int(number)
+
+
+def save_parameters(out_path, parameters_np):
+    """Save model paramters to file.
+
+    :param model: The model to serialize.
+    :type model: torch.nn.Module
+    :param out_path: The path to save to.
+    :type out_path: str
+    """
+    helper.save(parameters_np, out_path)
+
+
+def init_seed(out_path="seed.npz"):
+    """Initialize seed model and save it to file.
+
+    :param out_path: The path to save the seed model to.
+    :type out_path: str
+    """
+    # This calls get_parameters in the flower client which needs to be implemented.
+    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
+    save_parameters(out_path, parameters_np)
+
+
+def train(in_model_path, out_model_path):
+    """Complete a model update.
+
+    Load model paramters from in_model_path (managed by the FEDn client),
+    perform a model update through the flower client, and write updated paramters
+    to out_model_path (picked up by the FEDn client).
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_model_path: The path to save the output model to.
+    :type out_model_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    # Train on flower client
+    params, num_examples = flwr_adapter.train(
+        parameters=parameters_np, partition_id=_get_node_id(), config={}
+    )
+
+    # Metadata needed for aggregation server side
+    metadata = {
+        # num_examples are mandatory
+        "num_examples": num_examples,
+    }
+
+    # Save JSON metadata file (mandatory)
+    save_metadata(metadata, out_model_path)
+
+    # Save model update (mandatory)
+    save_parameters(out_model_path, params)
+
+
+def validate(in_model_path, out_json_path, data_path=None):
+    """Validate model on the clients test dataset.
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_json_path: The path to save the output JSON to.
+    :type out_json_path: str
+    :param data_path: The path to the data file.
+    :type data_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
+
+    # JSON schema
+    report = {
+        "test_loss": loss,
+        "test_accuracy": accuracy,
+    }
+    print(f"Loss: {loss}, accuracy: {accuracy}")
+    # Save JSON
+    save_metrics(report, out_json_path)
+
+
+if __name__ == "__main__":
+    fire.Fire(
+        {
+            "init_seed": init_seed,
+            "train": train,
+            "validate": validate,
+        }
+    )
diff --git a/examples/flower-client/package/fedn.yaml b/examples/flower-client/package/fedn.yaml
new file mode 100644
index 000000000..9fb33f18f
--- /dev/null
+++ b/examples/flower-client/package/fedn.yaml
@@ -0,0 +1,8 @@
+python_env: python_env.yaml
+entry_points:
+  build:
+    command: python entrypoint.py init_seed
+  train:
+    command: python entrypoint.py train
+  validate:
+    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/package/flwr_client.py b/examples/flower-client/package/flwr_client.py
new file mode 100644
index 000000000..297df3ca7
--- /dev/null
+++ b/examples/flower-client/package/flwr_client.py
@@ -0,0 +1,41 @@
+"""Flower client code using the ClientApp abstraction.
+Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
+"""
+
+from flwr.client import ClientApp, NumPyClient
+from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
+                       train)
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+    def __init__(self, cid) -> None:
+        super().__init__()
+        self.net = Net().to(DEVICE)
+        self.trainloader, self.testloader = load_data(
+            partition_id=int(cid), num_clients=10
+        )
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def fit(self, parameters, config):
+        set_weights(self.net, parameters)
+        train(self.net, self.trainloader, epochs=3)
+        return get_weights(self.net), len(self.trainloader.dataset), {}
+
+    def evaluate(self, parameters, config):
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.testloader)
+        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient(cid).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+)
diff --git a/examples/flower-client/package/flwr_task.py b/examples/flower-client/package/flwr_task.py
new file mode 100644
index 000000000..dea53d9cc
--- /dev/null
+++ b/examples/flower-client/package/flwr_task.py
@@ -0,0 +1,95 @@
+"""Flower client code for helper functions.
+Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
+"""
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from flwr_datasets import FederatedDataset
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, Normalize, ToTensor
+from tqdm import tqdm
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+
+    def __init__(self) -> None:
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def load_data(partition_id, num_clients):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose(
+        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
+
+
+def train(net, trainloader, epochs):
+    """Train the model on the training set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    for _ in range(epochs):
+        for batch in tqdm(trainloader, "Training"):
+            images = batch["img"]
+            labels = batch["label"]
+            optimizer.zero_grad()
+            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
+            optimizer.step()
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for batch in tqdm(testloader, "Testing"):
+            images = batch["img"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def get_weights(net):
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+def set_weights(net, parameters):
+    params_dict = zip(net.state_dict().keys(), parameters)
+    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/package/python_env.yaml b/examples/flower-client/package/python_env.yaml
new file mode 100644
index 000000000..b06804313
--- /dev/null
+++ b/examples/flower-client/package/python_env.yaml
@@ -0,0 +1,11 @@
+name: flower-client
+build_dependencies:
+  - pip
+  - setuptools
+  - wheel==0.37.1
+dependencies:
+  - torch==2.2.1
+  - torchvision==0.17.1
+  - fire==0.3.1
+  - fedn[flower]==0.9.0b2
+  - flwr-datasets[vision]==0.1.0
\ No newline at end of file

From 60ff02a951cbbedf51ebb4090a871db58a854180 Mon Sep 17 00:00:00 2001
From: viktorvaladi <viktor.valadi@hotmail.se>
Date: Thu, 18 Apr 2024 14:06:35 +0200
Subject: [PATCH 2/5] update docs

---
 examples/flower-client/README.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/flower-client/README.rst b/examples/flower-client/README.rst
index 364c80d3a..3231285a0 100644
--- a/examples/flower-client/README.rst
+++ b/examples/flower-client/README.rst
@@ -113,9 +113,9 @@ Then start the client (using Docker)
 Scaling to multiple clients
 ------------------------------------------------------------------
 
-To scale the experiment with additional clients on the same host, simply execute the run 
-command again from another terminal. If running from another host add another 'client.yaml', 
-install fedn and execute the run command. In both cases inject a client number as an environment 
+To scale the experiment with additional clients on the same host, execute the run command
+again from another terminal. If running from another host, add another 'client.yaml', install 
+fedn, and execute the run command. In both cases inject a client number as an environment 
 varible which is used for distributing data (see 'flwr_task.py').
 
 For Unix Operating Systems:

From 62884227b305dd24c269e17894042dd02c858351 Mon Sep 17 00:00:00 2001
From: viktorvaladi <viktor.valadi@hotmail.se>
Date: Thu, 18 Apr 2024 14:08:46 +0200
Subject: [PATCH 3/5] cleanup generated files

---
 .../client835-20240418-111128/entrypoint.py   | 107 ------------------
 .../client835-20240418-111128/fedn.yaml       |   8 --
 .../client835-20240418-111128/flwr_client.py  |  41 -------
 .../client835-20240418-111128/flwr_task.py    |  95 ----------------
 .../client835-20240418-111128/python_env.yaml |  11 --
 5 files changed, 262 deletions(-)
 delete mode 100755 examples/flower-client/client835-20240418-111128/entrypoint.py
 delete mode 100644 examples/flower-client/client835-20240418-111128/fedn.yaml
 delete mode 100644 examples/flower-client/client835-20240418-111128/flwr_client.py
 delete mode 100644 examples/flower-client/client835-20240418-111128/flwr_task.py
 delete mode 100644 examples/flower-client/client835-20240418-111128/python_env.yaml

diff --git a/examples/flower-client/client835-20240418-111128/entrypoint.py b/examples/flower-client/client835-20240418-111128/entrypoint.py
deleted file mode 100755
index 24f20773e..000000000
--- a/examples/flower-client/client835-20240418-111128/entrypoint.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-
-import fire
-from flwr_client import app
-
-from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
-
-HELPER_MODULE = "numpyhelper"
-helper = get_helper(HELPER_MODULE)
-
-flwr_adapter = FlwrClientAppAdapter(app)
-
-
-def _get_node_id():
-    """Get client number from environment variable."""
-
-    number = os.environ.get("CLIENT_NUMBER", "0")
-    return int(number)
-
-
-def save_parameters(out_path, parameters_np):
-    """Save model paramters to file.
-
-    :param model: The model to serialize.
-    :type model: torch.nn.Module
-    :param out_path: The path to save to.
-    :type out_path: str
-    """
-    helper.save(parameters_np, out_path)
-
-
-def init_seed(out_path="seed.npz"):
-    """Initialize seed model and save it to file.
-
-    :param out_path: The path to save the seed model to.
-    :type out_path: str
-    """
-    # This calls get_parameters in the flower client which needs to be implemented.
-    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
-    save_parameters(out_path, parameters_np)
-
-
-def train(in_model_path, out_model_path):
-    """Complete a model update.
-
-    Load model paramters from in_model_path (managed by the FEDn client),
-    perform a model update through the flower client, and write updated paramters
-    to out_model_path (picked up by the FEDn client).
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_model_path: The path to save the output model to.
-    :type out_model_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    # Train on flower client
-    params, num_examples = flwr_adapter.train(
-        parameters=parameters_np, partition_id=_get_node_id(), config={}
-    )
-
-    # Metadata needed for aggregation server side
-    metadata = {
-        # num_examples are mandatory
-        "num_examples": num_examples,
-    }
-
-    # Save JSON metadata file (mandatory)
-    save_metadata(metadata, out_model_path)
-
-    # Save model update (mandatory)
-    save_parameters(out_model_path, params)
-
-
-def validate(in_model_path, out_json_path, data_path=None):
-    """Validate model on the clients test dataset.
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_json_path: The path to save the output JSON to.
-    :type out_json_path: str
-    :param data_path: The path to the data file.
-    :type data_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
-
-    # JSON schema
-    report = {
-        "test_loss": loss,
-        "test_accuracy": accuracy,
-    }
-    print(f"Loss: {loss}, accuracy: {accuracy}")
-    # Save JSON
-    save_metrics(report, out_json_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(
-        {
-            "init_seed": init_seed,
-            "train": train,
-            "validate": validate,
-        }
-    )
diff --git a/examples/flower-client/client835-20240418-111128/fedn.yaml b/examples/flower-client/client835-20240418-111128/fedn.yaml
deleted file mode 100644
index 9fb33f18f..000000000
--- a/examples/flower-client/client835-20240418-111128/fedn.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-python_env: python_env.yaml
-entry_points:
-  build:
-    command: python entrypoint.py init_seed
-  train:
-    command: python entrypoint.py train
-  validate:
-    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/client835-20240418-111128/flwr_client.py b/examples/flower-client/client835-20240418-111128/flwr_client.py
deleted file mode 100644
index 297df3ca7..000000000
--- a/examples/flower-client/client835-20240418-111128/flwr_client.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Flower client code using the ClientApp abstraction.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from flwr.client import ClientApp, NumPyClient
-from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
-                       train)
-
-
-# Define FlowerClient and client_fn
-class FlowerClient(NumPyClient):
-    def __init__(self, cid) -> None:
-        super().__init__()
-        self.net = Net().to(DEVICE)
-        self.trainloader, self.testloader = load_data(
-            partition_id=int(cid), num_clients=10
-        )
-
-    def get_parameters(self, config):
-        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
-
-    def fit(self, parameters, config):
-        set_weights(self.net, parameters)
-        train(self.net, self.trainloader, epochs=3)
-        return get_weights(self.net), len(self.trainloader.dataset), {}
-
-    def evaluate(self, parameters, config):
-        set_weights(self.net, parameters)
-        loss, accuracy = test(self.net, self.testloader)
-        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
-
-
-def client_fn(cid: str):
-    """Create and return an instance of Flower `Client`."""
-    return FlowerClient(cid).to_client()
-
-
-# Flower ClientApp
-app = ClientApp(
-    client_fn=client_fn,
-)
diff --git a/examples/flower-client/client835-20240418-111128/flwr_task.py b/examples/flower-client/client835-20240418-111128/flwr_task.py
deleted file mode 100644
index dea53d9cc..000000000
--- a/examples/flower-client/client835-20240418-111128/flwr_task.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Flower client code for helper functions.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from flwr_datasets import FederatedDataset
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm import tqdm
-
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
-
-    def __init__(self) -> None:
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-
-def load_data(partition_id, num_clients):
-    """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
-    partition = fds.load_partition(partition_id)
-    # Divide data on each node: 80% train, 20% test
-    partition_train_test = partition.train_test_split(test_size=0.2)
-    pytorch_transforms = Compose(
-        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
-    )
-
-    def apply_transforms(batch):
-        """Apply transforms to the partition from FederatedDataset."""
-        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
-        return batch
-
-    partition_train_test = partition_train_test.with_transform(apply_transforms)
-    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
-    testloader = DataLoader(partition_train_test["test"], batch_size=32)
-    return trainloader, testloader
-
-
-def train(net, trainloader, epochs):
-    """Train the model on the training set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-    for _ in range(epochs):
-        for batch in tqdm(trainloader, "Training"):
-            images = batch["img"]
-            labels = batch["label"]
-            optimizer.zero_grad()
-            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
-            optimizer.step()
-
-
-def test(net, testloader):
-    """Validate the model on the test set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for batch in tqdm(testloader, "Testing"):
-            images = batch["img"].to(DEVICE)
-            labels = batch["label"].to(DEVICE)
-            outputs = net(images)
-            loss += criterion(outputs, labels).item()
-            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy
-
-
-def get_weights(net):
-    return [val.cpu().numpy() for _, val in net.state_dict().items()]
-
-
-def set_weights(net, parameters):
-    params_dict = zip(net.state_dict().keys(), parameters)
-    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/client835-20240418-111128/python_env.yaml b/examples/flower-client/client835-20240418-111128/python_env.yaml
deleted file mode 100644
index b06804313..000000000
--- a/examples/flower-client/client835-20240418-111128/python_env.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: flower-client
-build_dependencies:
-  - pip
-  - setuptools
-  - wheel==0.37.1
-dependencies:
-  - torch==2.2.1
-  - torchvision==0.17.1
-  - fire==0.3.1
-  - fedn[flower]==0.9.0b2
-  - flwr-datasets[vision]==0.1.0
\ No newline at end of file

From 78c254e198cfd2c65c673f6bd09b7ffccaa2580c Mon Sep 17 00:00:00 2001
From: viktorvaladi <viktor.valadi@hotmail.se>
Date: Thu, 18 Apr 2024 14:09:29 +0200
Subject: [PATCH 4/5] cleanup generated files

---
 examples/flower-client/package/entrypoint.py  | 107 ------------------
 examples/flower-client/package/fedn.yaml      |   8 --
 examples/flower-client/package/flwr_client.py |  41 -------
 examples/flower-client/package/flwr_task.py   |  95 ----------------
 .../flower-client/package/python_env.yaml     |  11 --
 5 files changed, 262 deletions(-)
 delete mode 100755 examples/flower-client/package/entrypoint.py
 delete mode 100644 examples/flower-client/package/fedn.yaml
 delete mode 100644 examples/flower-client/package/flwr_client.py
 delete mode 100644 examples/flower-client/package/flwr_task.py
 delete mode 100644 examples/flower-client/package/python_env.yaml

diff --git a/examples/flower-client/package/entrypoint.py b/examples/flower-client/package/entrypoint.py
deleted file mode 100755
index 24f20773e..000000000
--- a/examples/flower-client/package/entrypoint.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-
-import fire
-from flwr_client import app
-
-from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
-
-HELPER_MODULE = "numpyhelper"
-helper = get_helper(HELPER_MODULE)
-
-flwr_adapter = FlwrClientAppAdapter(app)
-
-
-def _get_node_id():
-    """Get client number from environment variable."""
-
-    number = os.environ.get("CLIENT_NUMBER", "0")
-    return int(number)
-
-
-def save_parameters(out_path, parameters_np):
-    """Save model paramters to file.
-
-    :param model: The model to serialize.
-    :type model: torch.nn.Module
-    :param out_path: The path to save to.
-    :type out_path: str
-    """
-    helper.save(parameters_np, out_path)
-
-
-def init_seed(out_path="seed.npz"):
-    """Initialize seed model and save it to file.
-
-    :param out_path: The path to save the seed model to.
-    :type out_path: str
-    """
-    # This calls get_parameters in the flower client which needs to be implemented.
-    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id(), config={})
-    save_parameters(out_path, parameters_np)
-
-
-def train(in_model_path, out_model_path):
-    """Complete a model update.
-
-    Load model paramters from in_model_path (managed by the FEDn client),
-    perform a model update through the flower client, and write updated paramters
-    to out_model_path (picked up by the FEDn client).
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_model_path: The path to save the output model to.
-    :type out_model_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    # Train on flower client
-    params, num_examples = flwr_adapter.train(
-        parameters=parameters_np, partition_id=_get_node_id(), config={}
-    )
-
-    # Metadata needed for aggregation server side
-    metadata = {
-        # num_examples are mandatory
-        "num_examples": num_examples,
-    }
-
-    # Save JSON metadata file (mandatory)
-    save_metadata(metadata, out_model_path)
-
-    # Save model update (mandatory)
-    save_parameters(out_model_path, params)
-
-
-def validate(in_model_path, out_json_path, data_path=None):
-    """Validate model on the clients test dataset.
-
-    :param in_model_path: The path to the input model.
-    :type in_model_path: str
-    :param out_json_path: The path to save the output JSON to.
-    :type out_json_path: str
-    :param data_path: The path to the data file.
-    :type data_path: str
-    """
-    parameters_np = helper.load(in_model_path)
-
-    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id(), config={})
-
-    # JSON schema
-    report = {
-        "test_loss": loss,
-        "test_accuracy": accuracy,
-    }
-    print(f"Loss: {loss}, accuracy: {accuracy}")
-    # Save JSON
-    save_metrics(report, out_json_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(
-        {
-            "init_seed": init_seed,
-            "train": train,
-            "validate": validate,
-        }
-    )
diff --git a/examples/flower-client/package/fedn.yaml b/examples/flower-client/package/fedn.yaml
deleted file mode 100644
index 9fb33f18f..000000000
--- a/examples/flower-client/package/fedn.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-python_env: python_env.yaml
-entry_points:
-  build:
-    command: python entrypoint.py init_seed
-  train:
-    command: python entrypoint.py train
-  validate:
-    command: python entrypoint.py validate
\ No newline at end of file
diff --git a/examples/flower-client/package/flwr_client.py b/examples/flower-client/package/flwr_client.py
deleted file mode 100644
index 297df3ca7..000000000
--- a/examples/flower-client/package/flwr_client.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Flower client code using the ClientApp abstraction.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from flwr.client import ClientApp, NumPyClient
-from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
-                       train)
-
-
-# Define FlowerClient and client_fn
-class FlowerClient(NumPyClient):
-    def __init__(self, cid) -> None:
-        super().__init__()
-        self.net = Net().to(DEVICE)
-        self.trainloader, self.testloader = load_data(
-            partition_id=int(cid), num_clients=10
-        )
-
-    def get_parameters(self, config):
-        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
-
-    def fit(self, parameters, config):
-        set_weights(self.net, parameters)
-        train(self.net, self.trainloader, epochs=3)
-        return get_weights(self.net), len(self.trainloader.dataset), {}
-
-    def evaluate(self, parameters, config):
-        set_weights(self.net, parameters)
-        loss, accuracy = test(self.net, self.testloader)
-        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
-
-
-def client_fn(cid: str):
-    """Create and return an instance of Flower `Client`."""
-    return FlowerClient(cid).to_client()
-
-
-# Flower ClientApp
-app = ClientApp(
-    client_fn=client_fn,
-)
diff --git a/examples/flower-client/package/flwr_task.py b/examples/flower-client/package/flwr_task.py
deleted file mode 100644
index dea53d9cc..000000000
--- a/examples/flower-client/package/flwr_task.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Flower client code for helper functions.
-Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
-"""
-
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from flwr_datasets import FederatedDataset
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm import tqdm
-
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
-
-    def __init__(self) -> None:
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-
-def load_data(partition_id, num_clients):
-    """Load partition CIFAR10 data."""
-    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_clients})
-    partition = fds.load_partition(partition_id)
-    # Divide data on each node: 80% train, 20% test
-    partition_train_test = partition.train_test_split(test_size=0.2)
-    pytorch_transforms = Compose(
-        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
-    )
-
-    def apply_transforms(batch):
-        """Apply transforms to the partition from FederatedDataset."""
-        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
-        return batch
-
-    partition_train_test = partition_train_test.with_transform(apply_transforms)
-    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
-    testloader = DataLoader(partition_train_test["test"], batch_size=32)
-    return trainloader, testloader
-
-
-def train(net, trainloader, epochs):
-    """Train the model on the training set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-    for _ in range(epochs):
-        for batch in tqdm(trainloader, "Training"):
-            images = batch["img"]
-            labels = batch["label"]
-            optimizer.zero_grad()
-            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
-            optimizer.step()
-
-
-def test(net, testloader):
-    """Validate the model on the test set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for batch in tqdm(testloader, "Testing"):
-            images = batch["img"].to(DEVICE)
-            labels = batch["label"].to(DEVICE)
-            outputs = net(images)
-            loss += criterion(outputs, labels).item()
-            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy
-
-
-def get_weights(net):
-    return [val.cpu().numpy() for _, val in net.state_dict().items()]
-
-
-def set_weights(net, parameters):
-    params_dict = zip(net.state_dict().keys(), parameters)
-    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-    net.load_state_dict(state_dict, strict=True)
diff --git a/examples/flower-client/package/python_env.yaml b/examples/flower-client/package/python_env.yaml
deleted file mode 100644
index b06804313..000000000
--- a/examples/flower-client/package/python_env.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: flower-client
-build_dependencies:
-  - pip
-  - setuptools
-  - wheel==0.37.1
-dependencies:
-  - torch==2.2.1
-  - torchvision==0.17.1
-  - fire==0.3.1
-  - fedn[flower]==0.9.0b2
-  - flwr-datasets[vision]==0.1.0
\ No newline at end of file

From 932e8c1b37eeeb6d03cfefb12f7578bb0fb7be4c Mon Sep 17 00:00:00 2001
From: Fredrik Wrede <fredrik@scaleoutsystems.com>
Date: Thu, 18 Apr 2024 12:53:06 +0000
Subject: [PATCH 5/5] fixes

---
 examples/flower-client/README.rst | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/examples/flower-client/README.rst b/examples/flower-client/README.rst
index 3231285a0..dfe0bca8f 100644
--- a/examples/flower-client/README.rst
+++ b/examples/flower-client/README.rst
@@ -1,12 +1,12 @@
 FEDn Project: Flower ClientApps in FEDn
--------------------------------
+---------------------------------------
 
 This example demonstrates how to run a Flower 'ClientApp' on FEDn.
 
 The FEDn compute package 'client/entrypoint' 
 uses a built-in Flower compatibiltiy adapter for convenient wrapping of the Flower client.
 See `flwr_client.py` and `flwr_task.py` for the Flower client code (which is adapted from 
-https://github.com/adap/flower/tree/main/examples/app-pytorch). 
+https://github.com/adap/flower/tree/main/examples/app-pytorch).
 
 
 Running the example
@@ -42,19 +42,19 @@ Next, generate a seed model (the first model in the global model trail):
 
    fedn run build --path client
 
-Next, you will upload the compute package and seed model to
+This creates a seed.npz in the root of the project folder. Next, you will upload the compute package and seed model to
 a FEDn network. Here you have two main options: using FEDn Studio 
-(recommended for new users), or a pseudo-local deployment
+(recommended for new users), or a self-managed pseudo-distributed deployment
 on your own machine. 
 
 If you are using FEDn Studio (recommended):
------------------------------------------------------
+-------------------------------------------
 
 Follow instructions here to register for Studio and start a project: https://fedn.readthedocs.io/en/stable/studio.html.
 
 In your Studio project: 
 
-- From the "Sessions" menu, upload the compute package and seed model. 
+- From the "Sessions" menu, upload the compute package (package.tgz) and seed model (seed.npz). 
 - Register a client and obtain the corresponding 'client.yaml'.  
 
 On your local machine / client, start the FEDn client: 
@@ -72,15 +72,14 @@ Or, if you prefer to use Docker (this might take a long time):
    docker run \
    -v $PWD/client.yaml:/app/client.yaml \
    -e CLIENT_NUMBER=0 \
-   -e FEDN_AUTH_SCHEME=Bearer \
    -e FEDN_PACKAGE_EXTRACT_DIR=package \
-   ghcr.io/scaleoutsystems/fedn/fedn:master run client -in client.yaml --secure=True --force-ssl
+   ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml --secure=True --force-ssl
 
 
-If you are running FEDn in pseudo-local mode:
-------------------------------------------------------------------
+If you are running FEDn in local development mode:
+--------------------------------------------------
 
-Deploy a FEDn network on local host (see `https://fedn.readthedocs.io/en/stable/quickstart.html`). 
+Deploy a FEDn network on local host (see `https://fedn.readthedocs.io/en/stable/quickstart.html#local-development-deployment-using-docker-compose`). 
 
 Use the FEDn API Client to initalize FEDn with the compute package and seed model: 
 
@@ -105,9 +104,8 @@ Then start the client (using Docker)
    -v $PWD/client.yaml:/app/client.yaml \
    --network=fedn_default \
    -e CLIENT_NUMBER=0 \
-   -e FEDN_AUTH_SCHEME=Bearer \
    -e FEDN_PACKAGE_EXTRACT_DIR=package \
-   ghcr.io/scaleoutsystems/fedn/fedn:master run client -in client.yaml
+   ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml
 
 
 Scaling to multiple clients